The Artificial Intelligence (AI)  industry has experienced tremendous growth and buzz, with Generative AI leading the charge. However, it's crucial to acknowledge that these impressive technologies are only part of the larger puzzle. In many situations, businesses require different technologies to achieve their desired outcomes.

To better understand the different areas of AI, it’s helpful to clarify some of the terminology. NLT (Natural Language Technology) is an umbrella term that encompasses NLP (Natural Language Processing), NLU (Natural Language Understanding), and NLG (Natural Language Generation), as well as related fields such as computational linguistics and language modeling. NLP deals with the interaction between computers and humans using natural language and allows computers to process, analyze, and understand human language. NLU focuses on a computer’s ability to understand human language in a similar way to humans. Generative AI, or NLG, concentrates on the generation of text by computers and has various applications, including automating routine tasks for businesses and helping artists generate new ideas. LLM (Large Language Model), the technology behind most generative AI tools we use, is an AI model trained on a vast corpus of text that uses deep neural networks to capture patterns and relationships in text data.

While many of us use ChatGPT in our daily lives, it’s essential to remember that for many use cases, businesses require additional technologies. For instance, while Generative AI generates human-like text, it may not always accurately understand the meaning behind that text. Moreover, while it can create new content, it may not always accurately reflect the desired style and tone and may even result in “hallucinations” – generated text that is not grounded in reality or significantly different from the data. This is where NLU comes in, as it extracts meaning from text, making it an essential component in various use cases such as helping chatbots to understand the intent behind the customer’s query, analyzing customer product reviews, or categorizing news articles into topics.

Fine-tuning an LLM requires significant computational power, resources, and expertise in AI and NLP. Businesses must gather a large amount of relevant data, train the model on that data, and fine-tune it to their specific needs. This process is time-consuming, resource-intensive, and requires specialized skills and knowledge, which most businesses do not possess. Despite fine-tuning, there is no guarantee that the model will perform as desired, and businesses may still face challenges such as low accuracy, bias, or a lack of understanding of context.

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